While ChatGPT has achieved brand dominance faster than any technology in history, the definitive consumer AI product has likely not yet been built. The future of our interaction with AI is expected to evolve significantly beyond the current chatbot model.

I don't think that the future of how we interact with AI is going to be a chatbot. I just think that's way too limiting. I think the big shift will be what is reactive today to something that's proactive in the future.

This proactive AI will feature long-form memory, be multimodal, and offer solutions to users' needs without being explicitly prompted. The economic potential is vast and difficult to predict, similar to the early days of social media. David George notes that a decade ago, investors might have used Facebook and Google's monetization of about $20 per user as an upper bound for new internet companies. Today, those platforms earn closer to $200 per user in the developed world, demonstrating how early estimates can drastically underestimate long-term value.

Currently, ChatGPT has about a billion users but only monetizes fewer than 50 million. The strategy for monetizing the rest remains a key question. It will likely be a new, native format that hasn't been invented yet, much like the social feed ad was unimaginable before the feed itself became a dominant product.

We never would have predicted what a feed based advertisement is. No one would have known what that is because we didn't even know what the feed based product was. It turns out it's probably the best advertisement format in history.

David shares a personal "light bulb" moment when he used AI to research a specific baseball bat for his son. The task was complex, with many specifications, and would have been a mess on Google or Amazon. The AI, however, solved the problem perfectly. This highlights the potential for AI to execute complex tasks on our behalf across the web. While building these capabilities and necessary guardrails is a challenge, the potential consumer value is enormous. Active AI users already spend nearly 30 minutes a day with these products, signaling a strong foundation for future growth.

David George feels he diverges from his peers in his skepticism about the ultimate business models for enterprise AI. While he shares the general excitement for consumer applications, he believes the path to capturing value on the enterprise side is less clear than many assume.

Many proponents argue that AI will capture a huge portion of the white-collar labor market, a market much larger than the current software industry. David finds this argument a bit "hand-wavy." He notes that clear business models have only emerged in a few areas. Customer support is one, where pricing can be based on successfully completing a discrete task. Coding is another, where a consumption-based model fits well with how the developer world already operates. For most other applications, the business model is still to be determined.

When major technological shifts occur, it is tempting to believe that companies will capture all the new economic value. However, the reality is often much harder. David suggests starting with a different assumption.

90% of the technological surplus is going to go to the end users. Just start with that as the assumption whether it's consumer, whether it's enterprise.

An analogy is the steam engine. It was not priced based on the value of the 50 laborers it replaced. Competitive forces drove its price down, and the vast majority of the productivity gains went to the people who used the machines, not the people who made them. This same dynamic of consumer surplus is seen today with companies like Apple and Google; consumers receive far more value from an iPhone or Google Search than what they pay. Even though users will capture most of the surplus, the next generation of AI companies can still become the biggest businesses in the world because the capability gains are so immense.

Investing in technologies like robotics or small modular reactors presents a challenge: they have huge potential, but the timeline for success is uncertain. Waymo is a prime example of a technology that was discussed for a long time with little visible progress, until it suddenly became ubiquitous in cities like San Francisco. This raises the question of how to approach investing in these exciting, long-term opportunities.

The complexity of robotics is far greater than that of self-driving cars. A car's job is relatively defined: stay in a lane, avoid collisions, and follow speed limits. In contrast, a robot in a home has 'endless degrees of freedom,' tasked with everything from making coffee to doing laundry. Waymo's journey took roughly two decades, and robotics is expected to take a long time as well, despite advancements like generative AI.

The investment strategy involves an early-stage team studying the landscape, looking for the right team to back with a seed or Series A investment. At the growth stage, the key is to wait for a company to 'really start to work.' The signal for this is often undeniable customer demand.

Andreessen Horowitz's investment in Waymo illustrates this. They first invested in 2020 when Waymo sought outside capital. David George was initially skeptical due to the high valuation and long timeline. However, others on the team saw the immense potential.

This is autonomous driving. Are you kidding me? This is the mother of all markets. If they have the thing that can drive cars autonomously, it's going to be worth a ton. Stop overthinking it.

They made a small initial investment. The turning point came later when the cars were on the road and working effectively. The signal was overwhelming consumer preference. David explains, 'Anyone who was in San Francisco who had the choice was taken Waymo.' At that point, they made a much larger investment.

A surprising fact highlights Waymo's efficiency: they operate only 400 cars in San Francisco. Despite this small number, they have overtaken the market share of the roughly 50,000 Lyft drivers in the area. This demonstrates the power of having a fleet of fully utilized vehicles running optimal routes.

David George's investment philosophy is to pay fair prices for great companies. The art in this approach lies in recognizing potential greatness where others may not see its full extent. While many investors focus on financial analysis like margins and unit economics, David believes the real competitive edge comes from superior insights into a company's product, market, and people.

On the people side, David looks for a specific archetype of founder he calls the "Technical Terminator." These are individuals who start with deep technical expertise and then develop strong business and commercial skills over time. They are grounded in the product, which positions them well to innovate and figure out the next product area. Mark Zuckerberg and Elon Musk are well-known examples of this archetype.

I really like a certain archetype of founder. I call him the Technical Terminator... They start technical, and then you never know if these people are going to become commercially minded, excellent business people... And then over time they learn the business side.

Ali from Databricks serves as a prime example. He began as one of seven founders on the open-source project and was not the original CEO. Over time, he grew into the role and mastered the business side, including sales operations and hiring processes, demonstrating the trajectory of a Technical Terminator.

While many successful founders are technical, some markets call for a different archetype. Travis Kalanick at Uber is a key counterexample. That market was a pure battle against mayors and competitors, requiring a ruthlessly competitive and operationally intense leader. However, there are many technical founders who also become great business people. Examples include George Kurtz from CrowdStrike and Dave from Roblox, who was technically brilliant and deeply involved in the product but also proved to be ruthlessly competitive.

This competitive drive is a common thread, even when it's not obvious on the surface. Dylan from Figma is one of the nicest people in the industry, yet he is brutally competitive. Newer AI founders also show this trait. Shiv from Abridge, a practicing cardiologist building a tech company, told David he was planning to put a bed in his New York office to work all the time when he's in town. This combination of relentlessness, intensity, and technical or product understanding is what makes these founders so compelling. They pour everything into winning and are better equipped to navigate complex, changing markets.

When deciding whether to invest, the core philosophy centers on market leadership. This is based on an analogy from the movie 'Glengarry Glen Ross'.

Okay, guys, new contest. Here we go. First prize gets Cadillac. Second prize gets a set of steak knives. Third prize, you're fired.

This scene describes how most tech markets work. The vast majority of market cap creation goes to the market leader. This is true not just for consumer businesses with network effects, but also for enterprise companies. There is no real number two to Salesforce, Workday, or ServiceNow. Investing in the second or third player in those markets would have been painful.

However, during major technological shifts, markets can fragment in unexpected ways. The AI model industry, for instance, seems likely to play out more like the cloud industry, which is not a winner-take-all market. The primary reason is the sheer size of the market. It's so vast that it can support multiple massive players like AWS, Microsoft Azure, and GCP. Similarly, the AI model market is expected to be so large that it will support several profitable companies, even if one isn't the absolute leader in revenue. What probably won't be okay, however, is being number two in a specific application, like the dominant consumer chat interface.

The venture capital industry has become significantly more institutionalized and competitive. There are more firms, more money, and the talent level of competitors is often much higher, forcing investors to constantly improve. This raises the question of what it feels like to compete for a major investment in a promising company led by a top founder in a large market.

Reflecting on the industry's past, David recalls stories from his firm's founders, Mark and Ben. He notes with some nostalgia that competing in those earlier, less crowded times seems like it would have been a lot of fun. He says, "Wouldn't it have been fun to compete in that time? That would have been awesome."

The venture capital market has become more competitive and institutionalized, evolving from a small, bespoke asset class into a mature industry. This shift is evidenced by the dominance of technology companies in the global economy. Today, eight of the ten largest companies by market cap are tech companies, and seven of them were venture-backed.

This growth has turned the private markets into a significant asset class. David George notes that there is currently $5 trillion of private market capitalization, a figure that has increased tenfold in the last 10 years. This value represents almost a quarter of the entire S&P 500 and is more than half the value of the "Magnificent Seven" tech stocks. Given this scale, the industry now operates in the "big leagues" and must adapt accordingly.

A comparison with public markets highlights this change. In sectors like software, consumer, and fintech, fewer than five public companies are growing at 30%. In contrast, the average dollar-weighted growth in David's private portfolio is 112%. Furthermore, the number of public companies has halved in the last 20 years, and the quality of small-cap public companies is arguably much lower than what is available in the private markets today.

The competition in venture capital has intensified, creating a market structure that resembles a barbell. This means the market is dominated by two distinct ends.

On one side are the large, multi-stage firms with strong venture practices. Using a retail analogy, these are the superstores like Walmart and Amazon. They are the fiercest competitors, trying to identify special companies at the seed or Series A stage and hold onto them tightly.

On the other side of the barbell are the bespoke, specialized firms. These are like the Gucci or Prada stores, representing deep specialization. There is a lot of respect for these players, as well as for crossover investors who have successfully built private businesses within this world.

Winning deals in a growth stage business isn't about sensational, one-off moments. David George explains that success comes from years of relationship building. He shares an example of a founder who, after two years of engagement, came directly to his firm to make a deal. This provided a clean look at one of the best companies in the market.

Even in this ideal scenario, the firm still has to determine if the price is right. The key is having a unique perspective on the product or market that others might not see. The two years leading up to the deal are spent helping the company as if they were already investors. This includes assisting with finding candidates and customers, and demonstrating a deep understanding of their business.

David also recounts the story of investing in Figma. His partner, Peter Levine, was extremely passionate about getting the deal done, highlighting the firm's conviction. Peter felt they had missed the earlier opportunity and was determined not to let it slip by again.

He was like, we need this tomorrow. We gotta invest in Figma. We need this tomorrow. I don't know how we missed it. I was late to it. We just need a growth business and it was a growth deal and we should have done it. It's crazy. We did GitHub early. How did we not do this one? And he was just apoplectic. I need this.

David George recounts the story of investing in Figma. From the moment he joined his firm, Figma was on a shortlist of companies he loved. His team pursued a relationship with the founder, Dylan Field, helping him with a board search and connecting him with others in their network. When COVID-19 struck and the market seemed to be collapsing, Dylan called and said, "now's the time."

An internal debate ensued. David's team, looking through a traditional growth lens, felt the market for designers was too small to justify the $2 billion valuation. However, the firm's venture investors were adamant they were missing the point. They argued that the ratio of designers to engineers was doubling at modern companies, indicating a larger future market. More importantly, they saw a fusion happening between front-end engineering and design, meaning the market was not just for designers, but something much broader.

The two teams were speaking past each other. After sleeping on it, David concluded that while the market size required a leap of faith, Figma was an exceptional business with a great founder. He decided he was willing to take a risk on the market, but not on the quality of the business or its leadership. The deal went through and worked out very well.

David draws two key lessons from this experience. First, for the very best companies, you have to accept the price and decide if you're willing to take the associated risks. Second, successful growth investing depends on more than just numbers.

You need those product and market insights or you're just going to live in a spreadsheet and die in a spreadsheet. So everything that we've done to design a process of tightly integrating with our early stage teams has been in the spirit of optimizing insights around people, products and markets.

David George shares advice he received from his mentor, Bob Swan, about time management. At the end of each year, Bob and John Donahoe would review their calendars and cut 30% of their activities. This practice served two purposes: it ensured they were delegating responsibility to their teams and helped them gain personal leverage.

Bob Swan...gave me this really good advice that he and John Donahoe at the end of every year always went through an exercise where they spent two hours looking at their calendar from the year and then they had an objective of cutting 30% of stuff that was on their calendar.

David applies a similar intentionality to his own schedule. While his firm's investment business is roughly two-thirds known companies and one-third new ones, he wants his time allocation to be the inverse. He aims to spend only 20% of his time on established portfolio companies and 80% on new opportunities. This focus on new markets, particularly AI, is crucial for his continuous learning. He spends most of his days meeting with AI founders and employees to deepen his understanding.

To protect time for learning and deep thinking, he has started deliberately blocking off time on his calendar. He sets aside two-hour blocks on Tuesdays and Thursdays, plus additional 90-minute blocks twice a week. While these often get filled with pressing calls, their existence is critical. David finds that without this scheduled "think time," he can't get to the reading and learning he needs to do to develop his own ideas.

When meeting with new companies, David keeps his process efficient and focused. As a partner at a growth fund that meets around 30 companies a week, he personally takes about 10 of those meetings. His structure is designed to get to the core of the business quickly. He keeps introductions brief and immediately asks the founder to outline their strategy and vision in five minutes. This approach is effective because he comes prepared, having already reviewed their website and sometimes even spoken to customers. The remaining 20 minutes are dedicated to a Q&A session where he probes deeper.

I like to jump in and say, 'Hey, why don't you please spend five minutes explaining to me the strategy and your vision?' Because I've read your website, I know a little bit about the company. I've talked to some customers maybe, but what is the bigger thing? You tell me and then I just ask questions for 20 minutes.

For David, the ultimate compliment from a founder is an acknowledgement of his preparation, such as, "Thanks, you've done your research," or, "Hey, thanks for asking that question. That's pretty smart."

When reflecting on his career choice, the speaker explains that his primary motivation is a deep-seated curiosity. He mentions his wife would say he has a low attention span, but he sees this as an interest in many different things. His current role is a perfect outlet for this, offering a constant stream of new subjects to learn about.

How lucky are we? We get to sit and spend time with the entrepreneurs who are building the most interesting companies in the world right now. We get to learn about the most cutting edge technology stuff that if you were in the public markets or just in a job, you would never get a chance to learn about.

The core attraction is the opportunity to be around great founders as they explore innovative ideas. The love for learning combined with exposure to groundbreaking work is what makes his career so fulfilling.

David George explains that the investment business is driven by a scoreboard, and at Andreessen Horowitz (a16z), the expectation is to win. This competitive mindset is a core part of the firm's culture, which is intentionally cultivated by founders Marc Andreessen and Ben Horowitz. Every new employee must sign the firm's culture document, which outlines its core principles.

For the growth fund, David established a specific set of principles, including the idea that "we are the Yankees and we're going to act like it." This isn't about arrogance, but about recognizing the firm's incredible brand and the high standards that come with it.

What I mean by that is we're lucky enough to be a part of a firm that has an incredible brand. So we're going to run our team very, very high performance. If you're on the Yankees, you better be performing. This is the big stage.

Contrary to any outside perception of celebrity partners resting on their laurels, David found the firm to be intensely competitive and hard-working. He was able to build the growth fund's team and strategy within this optimistic culture, bringing his own learnings while also innovating. One key difference he implemented was the investment decision-making process. Instead of a traditional investment committee where partners might politic for votes, the growth fund adopted the venture team's model of a "single trigger puller."

This model encourages intellectual honesty and open debate. Team members are expected to disagree and then commit once a decision is made. The process removes the temptation to "sell" an investment for the wrong reasons and allows for a more open exploration of its merits. It also allows the team to be fast and iterative. David's first investment decision was made over breakfast with Marc and Scott, highlighting the informal yet rigorous nature of their process.

To foster this culture within his team of about 10 investors, David made a unique addition to their evaluation criteria.

As part of the team's promotion criteria, evaluation, et cetera, I've put in their contribution to collective investment judgment, entry level, from the start. This is part of your job. You better be contributing to our collective investment judgment.

This expectation pushes even the most junior members to find their voice and contribute, which he believes has made the team's overall decision-making better.

The optimal environment for growth investing is an early product cycle combined with a bad capital cycle. But if forced to choose, the most important factor is being at the outset of a new technological change or market wave. In retrospect, the period when mobile, cloud, SaaS, and e-commerce all emerged simultaneously was a great setup. In contrast, David George explains the biggest mistake investors made in 2021.

The biggest mistake from 2021 is that we were actually late product cycle and we just didn't realize it at the time. There was a bit of a head fake with COVID but we didn't realize we were late product cycle. And what that means in practice is the ideas are just worse, the market opportunities are worse, it's just harder to go be successful.

Right now, investors are focused on whether there's an AI bubble, but it's crucial to stay in the market because great companies will emerge. The past two years have likely been a great period for investing. For example, David's portfolio is growing 112% and was entered at 21 times revenue. He argues this is less risky than buying a slow-growing company in private equity for 15 times EBITDA because high growth de-risks so much.

However, the market consistently undervalues persistent high growth. The reason is that it's difficult for investors to build financial models that project high growth continuing for many years. It feels unnatural.

It is just so hard for any investor to build a five or ten year model where high growth persists. It's just not natural. No one built a financial model for Google or Visa that had them growing 20 years into existence at 15 or 20%. It would just be totally unnatural to do so.

This creates massive opportunities. For example, in 2009, consensus estimates for Apple's 2013 performance were off by a factor of three, despite it being the most covered company in the world. A company's growth persisting at a slightly higher rate than modeled can result in a valuation that is three times different. This is the mathematical and psychological reason to love high-growth investing.

A key question to ask when evaluating a company is: "Is the market demanding more of your product?" This is the difference between a "pull" business and a "push" business. Finding a pull business is magical because the demand is organic. A prime example is ChatGPT, which grew to a billion users through its brand, surprisingly without a network effect. Another is Roblox, which is especially powerful because it has two network effects.

This dynamic isn't limited to consumer products. The defense company Anduril is a pull business. A confluence of factors created immense demand: advanced AI and autonomy capabilities, expertise in navigating government from former Palantir and SpaceX employees, and a pressing geopolitical need.

Push businesses, on the other hand, require active selling and marketing. These companies tend to get harder to scale over time, not easier. David explains this challenge:

If you have to go sell or market your product, the bigger you get, often it gets harder.

This is particularly true for consumer companies that rely on advertising from Google or Facebook. These platforms are designed to capture more of the economic value over time, at the expense of the businesses advertising on them. While there are exceptions like TikTok, which pushed aggressively early on by advertising heavily on Facebook, the general rule holds true.

When assessing AI businesses, there are three main considerations. The first is the ease of customer acquisition. This is a must-have, whether it comes from viral growth, like with the company Cursor, or from a clear value proposition that drives sales, as seen with Abridge in the healthcare sector. Doctors love the product, which saves them time and creates a strong pull for adoption within hospital systems.

The second factor is customer behavior, specifically retention and engagement. It's important to look for durable usage patterns rather than "head fakes" where a product grows quickly but usage drops off as the novelty wears off. Companies like Cursor and Harvey demonstrate this durable behavior. For Harvey, which serves lawyers, engagement actually increased as AI models improved their reasoning capabilities. This step-change in usage coincided with the models' breakthroughs, confirming that better reasoning created a more valuable product for its legal users.

The third consideration is gross margins, where the perspective has shifted from traditional SaaS standards. In the past, a SaaS company needed over 70% gross margins. In the current AI landscape, low margins are viewed differently.

Now it's like a badge of honor to have low gross margins because we're like, oh, at least people are using your AI products. We get these pitches and they're like, I have an AI thing and I got 75% gross margins. I'm like, well no one's using the AI stuff then that doesn't really seem like an AI product to me.

There is an expectation that inference costs will decrease over time. While costs have gone down, token usage has simultaneously increased due to new reasoning abilities, so margin improvements haven't materialized yet. Still, there is a belief that as models improve and costs drop, these businesses will achieve healthy, albeit lower, margins than traditional SaaS companies, perhaps around 50%. The potential for massive impact and value capture makes this a worthwhile trade-off.

Every great company possesses either a unique product or unique distribution. The best companies in the world have both. Ideally, a product is so unique that it naturally leads to unique distribution. A recent example is Cursor, a product so good that people just gravitate towards it. Another is GitHub, which was so special that for a long time, they never talked to customers.

The first time I ever met GitHub, they were like, we got to tell you this, this is so awesome. We sold to Walmart and they're paying us 400,000 bucks and no one ever talked to them on the phone. We were like, wow, this is an incredibly magical product and an incredibly magical market. Just imagine if you had talked to them on the phone, what would they have paid you? If you just called them on the phone, they've probably paid you $4 million.

This highlights the importance of not just having a great product, but also having a founder who wants to optimize the situation. The founders of Cursor recognize what they have and are aggressively pursuing enterprise sales alongside their bottoms-up user growth. This combination is powerful. When David George's firm makes customer introductions for Cursor, the feedback is immediate and overwhelming. Every introduction leads directly to a proof of concept or a full sales deal. This pattern is a clear signal of something special happening.

After one of these emails he chimed in and it's a big list, it's like a hundred people on the list or something. He wrote 'product fucking market fit.' So now we're like, oh, you know, PMF is now PFMF. So when you see that, you know that's unique product, that's unique distribution and you have a founder or founding team or full set of employees who really wants to optimize it, that's magic.

The fundamental thesis behind scaling at Andreessen Horowitz is that scale allows the firm to bring more resources and power to entrepreneurs, increasing their chances of success. Initially, the entire firm would meet every Monday and Friday to hear all pitches together, from crypto to bio. All partners would listen and then debate each investment as a group.

However, this monolithic structure became inefficient over time. David George explains, "Dixon weighing in on a bio investment and vice versa probably doesn't make sense." Recognizing this, Ben Horowitz and Marc Andreessen decided to decentralize the firm, creating specialized investment teams for each fund, such as infrastructure, crypto, or bio. This shift was driven by two main factors. First, it allowed for deeper expertise around the table for each sector, which is an advantage for both making investment decisions and helping companies with their go-to-market strategies. Second, you cannot effectively scale an organization with 25 or 30 decision-makers trying to weigh in on every single investment.

While this decentralized model has been working well, there is a trade-off. For the growth fund, which invests across all sectors, the old process of having access to all information was valuable. A significant portion of the growth fund's capital is deployed into companies the firm has previously backed. Over half of the dollars invested are in pre-existing venture investments, and this rises to around 70% when including follow-on investments. This prior relationship provides what David calls "game film" on the company and founder.

Game film is not just numbers, it's how has the founder done this?

This deep, long-term knowledge of a founder's performance and decision-making is a crucial advantage when assessing later-stage investments.

When the growth fund started, the initial idea was to have zero reserves. The goal was to scrutinize every single dollar. While this proved impractical, the fund now reserves only a tiny amount. This is for small follow-on investments where their participation is important but they are not the lead investor.

For large potential future investments in a company, there are zero reserves. This is a deliberate choice to prevent lazy decision-making. If money were already set aside, the temptation would be to invest it without the same level of rigor. Instead, every potential follow-on is treated as a completely new investment. This approach is reflected in their largest investments, such as Databricks, SpaceX, and OpenAI, which are often spread across multiple funds. This design allows for flexibility to continue backing companies they are excited about.

The fund does not have target metrics for specific industries. The philosophy is that the best ideas should always win. However, the portfolio is closely tracked to ensure it reflects the best perceived opportunities for the next 10 years.

When it comes to selling private market investments, many investors rely on simple heuristics. One common model is the Fred Wilson approach of selling a third, holding a third, and holding the final third forever. David George acknowledges that selling is one of the hardest parts of the job and that such a simple model is sensible for very early-stage venture investors like Wilson.

For growth-stage investing, his firm uses its own semi-algorithmic approach. It incorporates simple qualitative questions to guide decisions. They ask if the founder is still running the company, which they value highly, and if the company is the undisputed market leader. If the answers are yes, they are biased to hold the investment longer. If not, they lean towards exiting sooner. They also try to assess the company's valuation versus its performance, though this is acknowledged to be very difficult.

When asked why his firm doesn't do buyouts, David explains it's a cultural decision. Their entire mission is to help the next generation of companies beat the incumbents. Buying an incumbent company and trying to extend its life and extract value is culturally antithetical to their purpose.

A business model shift is a powerful strategy for upstarts because it is very difficult for incumbents to react to. When a new model is also better, faster, and cheaper for the customer by an order of magnitude, the odds are stacked in the startup's favor. David George explains that this is what is so exciting about a company like Decagon in the customer support industry.

Beyond business models, two other key components give startups the best chance: a completely reimagined user interface (UI) and completely new sources of data. David uses Salesforce as an example of an incumbent with a painful, reactive UI that is essentially a sophisticated form checker. The future, powered by AI, will be proactive. It will analyze new sources of unstructured data from all customer interactions to guide salespeople.

A salesperson, you're going to log into your Salesforce and it's going to be like, 'Hey, these are the five customers that you have business that you should be doing. I've been monitoring what they've been doing online. There's a shift in this group. You got to be aware of it. I've drafted a call script. This person actually likes to be talked to on the phone. This person wants to engage via your AI email. I've drafted one for you. I've already taken a bunch of action on your behalf.'

This combination of a new UI, new data sources, and a new business model provides the best opportunity for a startup to finally unseat an incumbent like Salesforce. During the last major shift with SaaS and the cloud, the market revenue grew 7x, and that new share was split evenly between incumbents and startups. The more dramatic the shift in business model, UI, and data, the more likely the outcome will favor startups.

David reflects on the extraordinary sacrifices his parents made for him, something that has become incredibly clear now that he has his own children. Growing up in Kentucky, far from his current world, he feels many lucky breaks went his way. However, the dedication of his parents stands out the most. His father drove him to all his activities, from soccer to baseball to basketball, and stood on the sidelines watching, even in the rain. He believes these experiences were formative in making him who he is today.

The sacrifices my parents made for me are extraordinary. They're incredible. And now I see it with my kids because I have to do that work and I have such a greater appreciation for what my parents gave to me and the sacrifices they made.